


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE			: int				   =					"▁"

SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE			: Optional[int]				   =					{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

SCREAMING_SNAKE_CASE			: List[Any]				   =					{
    "google/pegasus-xsum": 512,
}


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      Tuple       					=     VOCAB_FILES_NAMES

       lowercase_					:      Union[str, Any]       					=     VOCAB_FILES_NAMES
       lowercase_					:      Dict       					=     PRETRAINED_VOCAB_FILES_MAP
       lowercase_					:      str       					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_					:      List[Any]       					=     ["""input_ids""", """attention_mask"""]
       def __init__(   self, lowerCamelCase, lowerCamelCase="<pad>", lowerCamelCase="</s>", lowerCamelCase="<unk>", lowerCamelCase="<mask_2>", lowerCamelCase="<mask_1>", lowerCamelCase=None, lowerCamelCase=1_03, lowerCamelCase = None, **lowerCamelCase, )			->  None:




           """simple docstring"""

           _lowercase			:				str 						=						offset
           if additional_special_tokens is not None:
               if not isinstance(lowerCamelCase, lowerCamelCase):
                   raise TypeError(
                       F'''additional_special_tokens should be of type {type(lowerCamelCase)}, but is'''
                       F''' {type(lowerCamelCase)}''')

               _lowercase			:				Any 						=						(
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   F'''<unk_{i}>''' for i in range(len(lowerCamelCase), self.offset - 1)
               ]

               if len(set(lowerCamelCase)) != len(lowerCamelCase):
                   raise ValueError(
                       'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                       F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''')
               _lowercase			:				Optional[int] 						=						additional_special_tokens_extended
           else:
               _lowercase			:				str 						=						[mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [F'''<unk_{i}>''' for i in range(2, self.offset)]

           _lowercase			:				Optional[int] 						=						{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=lowerCamelCase, unk_token=lowerCamelCase, mask_token=lowerCamelCase, pad_token=lowerCamelCase, mask_token_sent=lowerCamelCase, offset=lowerCamelCase, additional_special_tokens=lowerCamelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCamelCase, )
           _lowercase			:				str 						=						mask_token_sent
           _lowercase			:				Optional[int] 						=						vocab_file
           _lowercase			:				Optional[int] 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(lowerCamelCase)

           # add special tokens to encoder dict
           _lowercase			:				Dict[int, str] 						=						{
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
               self.encoder.update(
                   {
                       2: self.mask_token_sent,
                       3: self.mask_token,
                   })

           if self.offset > 0:
               # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
               # mask_token_sent is already added to list -> so start at 1
               self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1, self.offset - 1)})

           _lowercase			:				Dict[str, int] 						=						{v: k for k, v in self.encoder.items()}
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return len(self.sp_model) + self.offset
       def      UpperCamelCase	(   self)			->  Dict[str, int]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{self.convert_ids_to_tokens(lowerCamelCase): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab
       def __getstate__(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.__dict__.copy()
           _lowercase			:				Union[str, Any] 						=						None
           return state
       def __setstate__(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				Dict 						=						d

           # for backward compatibility
           if not hasattr(self, 'sp_model_kwargs'):
               _lowercase			:				Any 						=						{}

           _lowercase			:				Any 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           return self.sp_model.encode(lowerCamelCase, out_type=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  int:




           """simple docstring"""

           if token in self.decoder:
               return self.decoder[token]
           elif token in self.added_tokens_decoder:
               return self.added_tokens_decoder[token]
           _lowercase			:				Any 						=						self.sp_model.piece_to_id(lowerCamelCase)
           return sp_id + self.offset
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           if index in self.encoder:
               return self.encoder[index]
           elif index in self.added_tokens_encoder:
               return self.added_tokens_encoder[index]
           else:
               _lowercase			:				Tuple 						=						self.sp_model.IdToPiece(index - self.offset)
           return token
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						[]
           _lowercase			:				Optional[int] 						=						''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(lowerCamelCase) + token
                   _lowercase			:				List[str] 						=						[]
               else:
                   current_sub_tokens.append(lowerCamelCase)
           out_string += self.sp_model.decode(lowerCamelCase)
           return out_string.strip()
       def      UpperCamelCase	(   self, lowerCamelCase=False)			->  Optional[Any]:




           """simple docstring"""

           return 1
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				str 						=						set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = False)			->  List[int]:




           """simple docstring"""

           if already_has_special_tokens:
               return self._special_token_mask(lowerCamelCase)
           elif token_ids_a is None:
               return self._special_token_mask(lowerCamelCase) + [1]
           else:
               return self._special_token_mask(token_ids_a + token_ids_a) + [1]
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  List[int]:




           """simple docstring"""

           if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple[str]:




           """simple docstring"""

           if not os.path.isdir(lowerCamelCase):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
               return
           _lowercase			:				Tuple 						=						os.path.join(
               lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

           if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCamelCase) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file, lowerCamelCase)
           elif not os.path.isfile(self.vocab_file):
               with open(lowerCamelCase, 'wb') as fi:
                   _lowercase			:				Tuple 						=						self.sp_model.serialized_model_proto()
                   fi.write(lowerCamelCase)

           return (out_vocab_file,)








'''simple docstring'''







import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_     						=				'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_     						=				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_     						=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_     						=				re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_     						=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_     						=				[
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,UpperCAmelCase						)
   return [m.group(0						) for m in matches]







def 						_A      ( ):






   '''simple docstring'''







   A__       =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   A__       =     {
       config.replace('Config'      ,''						): model_type for model_type, config in config_maping_names.items()
   }

   # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)

   # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
   for attr_name in dir(UpperCAmelCase						):
      A__       =     None
      if _re_tf_models.match(UpperCAmelCase						) is not None:
         A__       =     tf_models
         A__       =     _re_tf_models.match(UpperCAmelCase						).groups()[0]
      elif _re_flax_models.match(UpperCAmelCase						) is not None:
         A__       =     flax_models
         A__       =     _re_flax_models.match(UpperCAmelCase						).groups()[0]
      elif _re_pt_models.match(UpperCAmelCase						) is not None:
         A__       =     pt_models
         A__       =     _re_pt_models.match(UpperCAmelCase						).groups()[0]

      if lookup_dict is not None:
         while len(UpperCAmelCase						) > 0:
            if attr_name in model_prefix_to_model_type:
               A__       =     True
               break
            # Try again after removing the last word in the name
            A__       =     ''.join(camel_case_split(UpperCAmelCase						)[:-1]						)

   A__       =     set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
   A__       =     list(UpperCAmelCase						)
   all_models.sort()

   A__       =     {'model_type': all_models}
   A__       =     [pt_models[t] for t in all_models]
   A__       =     [tf_models[t] for t in all_models]
   A__       =     [flax_models[t] for t in all_models]

   # Now let's use the auto-mapping names to make sure
   A__       =     {}
   for t in all_models:
      if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
         A__       =     'AutoProcessor'
      elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
         A__       =     'AutoTokenizer'
      elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
         A__       =     'AutoFeatureExtractor'
      else:
         # Default to AutoTokenizer if a model has nothing, for backward compatibility.
         A__       =     'AutoTokenizer'

   A__       =     [processors[t] for t in all_models]

   return pd.DataFrame(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     [
       transformers_module.models.auto.modeling_auto,
       transformers_module.models.auto.modeling_tf_auto,
       transformers_module.models.auto.modeling_flax_auto,
   ]
   for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
      A__       =     [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
      A__       =     [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
      # Loop through all three frameworks
      for module, cls, mapping in zip(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         # The type of pipeline may not exist in this framework
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						):
            continue
         # First extract all model_names
         A__       =     []
         for name in getattr(UpperCAmelCase      ,UpperCAmelCase						).values():
            if isinstance(UpperCAmelCase      ,UpperCAmelCase						):
               model_names.append(UpperCAmelCase						)
            else:
               model_names.extend(list(UpperCAmelCase						)						)

            # Add pipeline tag and auto model class for those models
         table.update({model_name: (pipeline_tag, cls) for model_name in model_names}						)

   return table







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_frameworks_table()
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   A__       =     hf_hub_download(
       'huggingface/transformers-metadata'      ,'pipeline_tags.json'      ,repo_type='dataset'      ,token=UpperCAmelCase						)
   A__       =     Dataset.from_json(UpperCAmelCase						)
   A__       =     {
       tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
       for i in range(len(UpperCAmelCase						)						)
   }
   A__       =     update_pipeline_and_auto_class_table(UpperCAmelCase						)

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   A__       =     sorted(table.keys()						)
   A__       =     pd.DataFrame(
       {
           'model_class': model_classes,
           'pipeline_tag': [table[m][0] for m in model_classes],
           'auto_class': [table[m][1] for m in model_classes],
       }						)
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   with tempfile.TemporaryDirectory() as tmp_dir:
      frameworks_dataset.to_json(os.path.join(UpperCAmelCase      ,'frameworks.json'						)						)
      tags_dataset.to_json(os.path.join(UpperCAmelCase      ,'pipeline_tags.json'						)						)

      if commit_sha is not None:
         A__       =     (
             F"""Update with commit {commit_sha}\n\nSee: """
             F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
         )
      else:
         A__       =     'Update'

      upload_folder(
          repo_id='huggingface/transformers-metadata'      ,folder_path=UpperCAmelCase      ,repo_type='dataset'      ,token=UpperCAmelCase      ,commit_message=UpperCAmelCase      ,)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   A__       =     transformers_module.pipelines.SUPPORTED_TASKS
   A__       =     []
   for key in pipeline_tasks:
      if key not in in_table:
         A__       =     pipeline_tasks[key]['pt']
         if isinstance(UpperCAmelCase      ,(list, tuple)						):
            A__       =     model[0]
         A__       =     model.__name__
         if model not in in_table.values():
            missing.append(UpperCAmelCase						)

   if len(UpperCAmelCase						) > 0:
      A__       =     ', '.join(UpperCAmelCase						)
      raise ValueError(
          'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
          F"""`utils/update_metadata.py`: {msg}. Please add them!"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
    parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
    parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
    lowerCAmelCase_     						=				parser.parse_args()

    if args.check_only:
        check_pipeline_tags()
    else:
        update_metadata(args.token, args.commit_sha)


